library(tidyverse)
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library(ggplot2)
library(here)
## here() starts at /Users/feliciacruz/Documents/MEDS/Winter_22/EDS_240/data-viz-final
library(janitor)
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## 
##     chisq.test, fisher.test
library(lubridate)
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## Attaching package: 'lubridate'
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## 
##     date, intersect, setdiff, union
library(stringr)
library(plotly)
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## Attaching package: 'plotly'
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cots <- read_csv(here("cots.csv")) %>% 
  clean_names()
## Rows: 1008 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Site, Habitat
## dbl (3): Year, Transect, COTS
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
cots$year <- as.factor(cots$year)
cots_summary <- cots %>%
  group_by(site, year) %>% 
  summarize(total = sum(cots))
## `summarise()` has grouped output by 'site'. You can override using the `.groups` argument.
# make totals df 
totals <- cots_summary %>% 
  group_by(year) %>% 
  summarize(total_count = sum(total)) 
g <- ggplot(data = cots_summary, aes(x = as.factor(year), y = total, fill = site)) +
  geom_col(position = "stack") +
  labs(title = "Crown of Thorns Sea Stars",
       subtitle = "Moorea, French Polynesia (2005-2018)",
       fill = "Site",
       x = "year") +
  scale_y_continuous(breaks = seq(0, 150, by = 25)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank()) +
  scale_fill_brewer(palette = "Set3") 

g

ggplotly(g,
         tooltip = c("total", "site")) %>% 
  layout(title = list(text = paste0('Crown of Thorns Sea Stars',
                                    '<br>',
                                    '<sup>',
                                    'Moorea, French Polynesia (2005-2018)',
                                    '</sup>')))
g_2 <- ggplot(data = cots_summary, aes(x = as.factor(year), y = total, fill = site)) +
  geom_col(position = "stack") +
  labs(title = "Crown of Thorns Sea Stars",
       subtitle = "Moorea, French Polynesia (2005-2018)",
       fill = "Site",
       x = "year") +
  scale_y_continuous(breaks = seq(0, 150, by = 25)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank()) +
  scale_fill_brewer(palette = "Set3") +
  geom_text(
    aes(label = stat(y), group = year), 
    stat = 'summary', fun = sum, vjust = -1, nudge_y = 2
  )
  
ggplotly(g_2,
         tooltip = c("total", "site"))  %>% 
  layout(title = list(text = paste0('Crown of Thorns Sea Stars',
                                    '<br>',
                                    '<sup>',
                                    'Moorea, French Polynesia (2005-2018)',
                                    '</sup>')))

testing 

# total coral line graph? 
coral <- read_csv(here("coral_pop_since_05.csv")) %>% 
  clean_names() %>% 
  as.data.frame() %>% 
  separate(date, c("year", "month"), "-")
## Rows: 285404 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Date, Location, Site, Habitat, Taxonomy / Substrate / Functional Group
## dbl (3): Transect, Quadrat, Percent Cover
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Warning: One or more parsing issues, see `problems()` for details
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [285404].
coral %>% 
  group_by(year) %>% 
  summarize(mean(percent_cover))
## # A tibble: 16 × 2
##    year          `mean(percent_cover)`
##    <chr>                         <dbl>
##  1 (285403 rows)                 NA   
##  2 2005                          18.0 
##  3 2006                          16.3 
##  4 2007                           3.49
##  5 2008                           3.70
##  6 2009                           3.70
##  7 2010                           3.63
##  8 2011                           3.45
##  9 2012                           3.45
## 10 2013                           3.23
## 11 2014                           2.78
## 12 2015                           2.78
## 13 2016                           3.04
## 14 2017                           3.13
## 15 2018                           3.12
## 16 2019                           3.12

Adding a dual y axis to show COTS and macroalgae ?

# explore macroalgae 
algae <- read_csv(here("benthic_algae.csv"))
## Rows: 187522 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): Year, Location, Site, Habitat, Taxonomy_Substrate_Functional_Group
## dbl  (3): Transect, Quadrat, Percent_Cover
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

group by site & year , sum total observations / 1000 -> annual site mean density

cots_density <-  cots %>% 
  group_by(site, year) %>% 
  summarize(density = (sum(cots)/1000)) # this should give annual site mean density 
## `summarise()` has grouped output by 'site'. You can override using the `.groups` argument.

plot density

ggplot(data = cots_density, aes(x = year, y = density)) +
  geom_point(aes(color = site)) +
  geom_line(aes(group = site, color = site)) +
  facet_wrap(~site) +
  labs(title = "Crown of Thorns Sea Stars - Annual Site Means",
       subtitle = "Moorea, French Polynesia (2005 - 2018)") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 80, hjust = 1))
Testing

Testing

From Figure 1, we can see